Role & Responsibilities :

- Design and architect scalable, secure, and high-performance big data platforms capable of handling large volumes of structured and unstructured data.

- Define end-to-end data architecture, data ingestion, processing, storage, integration, and analytics solutions.

- Develop and implement data pipelines using technologies such as Apache Spark, Kafka, Hadoop, Hive, and Airflow.

- Design cloud-based data platforms using AWS, Microsoft Azure, or Google Cloud Platform (GCP).

- Architect modern data solutions using platforms such as Databricks, Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.

- Establish standards and best practices for data modeling, data governance, data quality, security, and metadata management.

- Work closely with data engineers, data scientists, analysts, application teams, and business stakeholders to translate requirements into scalable technical solutions.

- Evaluate existing data architecture and identify opportunities for modernization, optimization, automation, and cost reduction.

- Design solutions for batch and real-time/streaming data processing.

- Ensure high availability, fault tolerance, disaster recovery, and performance of data platforms.

- Provide technical leadership and mentoring to data engineers and development teams.

Preferred Candidate Profile :

- 8 - 10 years of experience in data engineering, big data, cloud data platforms, or data architecture, with significant experience designing enterprise-scale data solutions.

- Strong understanding of Big Data architecture and distributed computing concepts.

- Hands-on experience with Apache Spark and at least one major big data ecosystem such as Hadoop/Hive.

- Strong experience with Python, Java, or Scala.

- Experience designing and implementing ETL/ELT and data pipelines.

- Strong knowledge of Kafka or other event-streaming technologies for real-time data processing.

- Experience with one or more cloud platforms: AWS, Azure, or GCP.

- Experience with modern data platforms such as Databricks, Snowflake, BigQuery, Redshift, or Azure Synapse.

- Strong knowledge of SQL, data modeling, data warehousing, data lakes, and lakehouse architecture.

- Understanding of data governance, data quality, security, metadata, and master data concepts.

- Experience with orchestration tools such as Airflow, Azure Data Factory, AWS Glue, or similar technologies.

- Experience providing technical leadership, architecture guidance, and mentoring to engineering teams.

- A Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field is preferred.